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Metric learning

Author: Aurélien Bellet; Amaury Habrard; Marc Sebban
Publisher: San Rafael, California : Morgan & Claypool Publishers, [2015]
Series: Synthesis lectures on artificial intelligence and machine learning, #30.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance  Read more...
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Details

Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Aurélien Bellet; Amaury Habrard; Marc Sebban
ISBN: 9781627053662 1627053662
OCLC Number: 903883121
Description: 1 online resource (xi, 139 pages) : illustrations.
Contents: 1. Introduction --
1.1 Metric learning in a nutshell --
1.2 Related topics --
1.3 Prerequisites and notations --
1.4 Outline --
2. Metrics --
2.1 General definitions --
2.2 Commonly used metrics --
2.2.1 Metrics for numerical data --
2.2.2 Metrics for structured data --
2.3 Metrics in machine learning and data mining --
3. Properties of metric learning algorithms --
4. Linear metric learning --
4.1 Mahalanobis distance learning --
4.1.1 Early approaches --
4.1.2 Regularized approaches --
4.2 Linear similarity learninG --
4.3 Large-scale metric learning --
4.3.1 Large n: online, stochastic and distributed optimization --
4.3.2 Large d: metric learning in high dimensions --
4.3.3 Large n and large d --
5. Nonlinear and local metric learning --
5.1 Nonlinear methods --
5.1.1 Kernelization of linear methods --
5.1.2 Learning nonlinear forms of metrics --
5.2 Learning multiple local metrics --
6. Metric learning for special settings --
6.1 Multi-task and transfer learning --
6.2 Learning to rank --
6.3 Semi-supervised learning --
6.3.1 Classic setting --
6.3.2 Domain adaptation --
6.4 Histogram data --
7. Metric learning for structured data --
7.1 String edit distance learning --
7.1.1 Probabilistic methods --
7.1.2 Gradient descent methods --
7.2 Tree and graph edit distance learning --
7.3 Metric learning for time series --
8. Generalization guarantees for metric learning --
8.1 Overview of existing work --
8.2 Consistency bounds for metric learning --
8.2.1 Definitions --
8.2.2 Bounds based on uniform stability --
8.2.3 Bounds based on algorithmic robustness --
8.3 Guarantees on classification performance --
8.3.1 Good similarity learning for linear classification --
8.3.2 Bounds based on Rademacher complexity --
9. Applications --
9.1 Computer vision --
9.2 Bioinformatics --
9.3 Information retrieval --
10. Conclusion --
10.1 Summary --
10.2 Outlook --
A. Proofs of chapter 8 --
Uniform stability --
Algorithmic robustness --
Similarity-based linear classifiers --
Bibliography --
Authors' biographies.
Series Title: Synthesis lectures on artificial intelligence and machine learning, #30.
Responsibility: Aurélien Bellet, Amaury Habrard, Marc Sebban.

Abstract:

Examines metric learning, a set of techniques to automatically learn similarity and distance functions from data. The authors provide a thorough review of the metric learning literature, covering  Read more...

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